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Applied Predictive Modeling
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a t...
Feature Engineering and Selection
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
An Introduction to Particle Accelerators
From the linear accelerators used for cancer therapy in hospitals, to the giant atom smashers at international laboratories, this book provides a simple introduction to particle accelerators.
ESSENTIALS OF AI AND SOFT COMPUTING
The book has been primarily designed for the beginners in the subject. It has been written from the students' perspective, making it easy to understand. The contents are briefly explained with the help of examples in a direct and a pragmatic approach. Each chapter begins with the basics and is standalone; the dependence of the chapters on previous concepts has been minimized. The text is aimed to balance the mix of notation and words in mathematical statements. Artificial Intelligence and Soft Computing topics are often expressed in terms of algorithms, hence key algorithms are introduced with their explanations. These algorithms are expressed in words and in an easy to understand form of st...
Optimization for Machine Learning
Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.
Pharmaceutical Statistics Using SAS
Introduces a range of data analysis problems encountered in drug development and illustrates them using case studies from actual pre-clinical experiments and clinical studies. Includes a discussion of methodological issues, practical advice from subject matter experts, and review of relevant regulatory guidelines.
Advanced Computing Techniques for Optimization in Cloud
This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud...
The Ordinal Society
Organizations now measure and rank nearly every aspect of our lives, using data to make predictions about our purchasing power, tastes, and character. The Ordinal Society shows how these predictions structure life chances, producing a hollow morality that launders familiar forms of social advantage into an illusion of merit.
Data Mining for the Social Sciences
"The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David ...
